CHANGE-POINT ESTIMATION UNDER ADAPTIVE SAMPLING

• 成果类型: Article
• 署名作者: Lan, Yan; Banerjee, Moulinath; Michailidis, George
• 署名单位: University of Michigan System; University of Michigan; Abbott Laboratories
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/08-AOS602
• 发表日期: 2009
• 页码: 1752-1791
• 关键词: Nonparametric regression; covariate; threshold; CURVES; models

摘要

We consider the problem of locating a jump discontinuity (chan-e-point) in a smooth parametric regression model with a bounded covariate. It is assumed that one can sample the covariate at different values and measure the corresponding responses. Budget constraints dictate that a total of n such measurements can be obtained. A multistage adaptive procedure is proposed, where at each stage an estimate of the change point is obtained and new points are sampled from its appropriately chosen neighborhood. It is shown that such procedures accelerate the rate of convergence of the least squares estimate of the change-point. Further, the asymptotic distribution of the estimate is derived using empirical processes techniques. The latter result provides guidelines on how to choose the tuning parameters of the multistage procedure in practice. The improved efficiency of the procedure is demonstrated using real and synthetic data. This problem is primarily motivated by applications in engineering systems.